*Contribution*

The current study aims to address solutions for data pre-processing and the input selection problem. As mentioned above, previous studies did not develop a robust model that is compatible with different datasets. The best combination of the input variables must be achieved before applying any data pre-processing or feature extraction techniques. The paper proposes a robust model which is capable of forecasting hourly electrical load demand with any given inputs. The inputs may include different combinations of previous demand values and weather parameters.

In addition, despite a combination of ANNs and ANFIS being discussed in previous research, training ANFIS was realized using a hybrid method. The proposed methodology employs meta-heuristic algorithms for ANFIS training and combines MLPNN, ANFIS, and meta-heuristics to increase the forecasting accuracy. Some research related to training ANFIS using meta-heuristics is given in [17–20].

The proposed methodology is described in Section 2 and the results are presented and discussed in Section 3. Finally, Section 4 is the conclusion of the present study.
